Abstract: A document processing device includes: a document classification section configured to sequentially apply a plurality of classification processes in a prescribed sequence to a plurality of documents to classify the plurality of documents into a plurality of groups; and a classification determination section configured to every time one of the plurality of classification processes is applied, determine whether or not each group contains two or more documents. The document classification section, after applying a preceding one of the plurality of classification processes, applies a succeeding one of the plurality of classification processes to the two or more documents in each group determined as containing the two or more documents. The prescribed sequence is an ascending order of an amount of calculation involved in the plurality of classification processes.

Abstract: An information processing method includes: obtaining a first evaluation result representing performance of a first machine learning model having learned using first learning data, the first evaluation result being calculated using first validation data; obtaining a second evaluation result representing performance of a second machine learning model having learned using second learning data, the second evaluation result being calculated using second validation data; and calculating, based on the first evaluation result and the second evaluation result, a comprehensive evaluation result representing performance of a single machine learning model including the first machine learning model and the second machine learning model, the performance of the single machine learning model being predicted when the single machine learning model is applied to unevaluated, unknown data relevant to a prescribed event.

Abstract: [Problem to be Solved] Provided is a computer system that can accurately evaluate data to be analyzed without adding training data. [Solution] The computer system forms, from a matrix based on a co-occurrence frequency of first data elements forming at least one piece of data out of a plurality of data and second data elements appearing in vicinity of the first data elements, vectors for a plurality of data elements as the first data elements, calculates similarities for the first data elements on the basis of the vectors, and sets evaluation values for the first data elements on the basis of evaluation values corrected in accordance with the similarities.

Abstract: A document processing device includes: a document classification section configured to sequentially apply a plurality of classification processes in a prescribed sequence to a plurality of documents to classify the plurality of documents into a plurality of groups; and a classification determination section configured to every time one of the plurality of classification processes is applied, determine whether or not each group contains two or more documents. The document classification section, after applying a preceding one of the plurality of classification processes, applies a succeeding one of the plurality of classification processes to the two or more documents in each group determined as containing the two or more documents. The prescribed sequence is an ascending order of an amount of calculation involved in the plurality of classification processes.

Abstract: To construct a learned model exhibiting a high generalization capability. A data set is stored in a memory of a computer. A controller of the computer executes: sampling processing for sampling first learning data from the data set; clustering processing for generating a plurality of clusters by clustering data included in the data set; selection processing for selecting second learning data from a cluster not including the first learning data among the plurality of clusters; and configuration processing for configuring learning data set including the first learning data and at least a part of the second learning data as the learning data set.

Abstract: [Problem to be Solved] Provided is a computer system that can accurately evaluate data to be analyzed without adding training data. [Solution] The computer system forms, from a matrix based on a co-occurrence frequency of first data elements forming at least one piece of data out of a plurality of data and second data elements appearing in vicinity of the first data elements, vectors for a plurality of data elements as the first data elements, calculates similarities for the first data elements on the basis of the vectors, and sets evaluation values for the first data elements on the basis of evaluation values corrected in accordance with the similarities.